To enhance the intelligence level and operational reliability of intelligent connected vehicle operation and maintenance, and to establish an efficient and precise predictive maintenance pattern. By using the digital twin modeling method, a dynamic mapping model of key components is built, and algorithms such as particle filtering and long short-term memory neural networks are combined to complete state recognition and life prediction. Taking the real vehicle platform as an example, multi-scenario verification is implemented to study the health assessment and maintenance response measures of key components during the degradation and evolution process. This technology features excellent fault early warning accuracy and robustness in environmental tolerance, providing a feasible technical channel for intelligent operation and maintenance.